



'''simple docstring'''






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
     from sagemaker import Session, TrainingJobAnalytics
     from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""						,					"""False"""      )      ) is not True						,					reason="""Skipping test because should only be run when releasing minor transformers version"""						,					)
@pytest.mark.usefixtures("""sm_env"""      )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    List[Any]      ):
      if self.framework == "pytorch":
        subprocess.run(
            F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()		,     encoding='utf-8'		,     check=__snake_case		,     )
      assert hasattr(self		,     'env'      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    Union[str, Any]=1      ):
      # creates estimator
      return HuggingFace(
          entry_point=self.script		,     source_dir=self.env.test_path		,     role=self.env.role		,     image_uri=self.env.image_uri		,     base_job_name=F"""{self.env.base_job_name}-single"""		,     instance_count=__snake_case		,     instance_type=self.instance_type		,     debugger_hook_config=__snake_case		,     hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}		,     metric_definitions=self.env.metric_definitions		,     py_version='py36'		,     )







    def      lowercase_			( self					:    int		,     __snake_case					:    Any      ):
      TrainingJobAnalytics(__snake_case      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )







    def      lowercase_			( self					:    List[Any]      ):
      # create estimator
      a		:   Optional[Any]	  =	self.create_estimator()

      # run training
      estimator.fit()

      # result dataframe
      a		:   Optional[Any]	  =	TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      a		:   Optional[Any]	  =	list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
      a		:   List[str]	  =	list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      a		:   Any	  =	(
          Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'		,     99_99_99      )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy      )
      assert all(t <= self.results['eval_loss'] for t in eval_loss      )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""		,     'w'      ) as outfile:
        json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}		,     __snake_case      )




'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )